diff options
Diffstat (limited to 'csit.infra.dash/app/cdash/report/graphs.py')
| -rw-r--r-- | csit.infra.dash/app/cdash/report/graphs.py | 42 |
1 files changed, 32 insertions, 10 deletions
diff --git a/csit.infra.dash/app/cdash/report/graphs.py b/csit.infra.dash/app/cdash/report/graphs.py index 44c57d4183..0627411d0f 100644 --- a/csit.infra.dash/app/cdash/report/graphs.py +++ b/csit.infra.dash/app/cdash/report/graphs.py @@ -14,11 +14,11 @@ """Implementation of graphs for iterative data. """ - import plotly.graph_objects as go import pandas as pd from copy import deepcopy +from numpy import percentile from ..utils.constants import Constants as C from ..utils.utils import get_color, get_hdrh_latencies @@ -74,7 +74,7 @@ def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame: def graph_iterative(data: pd.DataFrame, sel: list, layout: dict, - normalize: bool=False) -> tuple: + normalize: bool=False, remove_outliers: bool=False) -> tuple: """Generate the statistical box graph with iterative data (MRR, NDR and PDR, for PDR also Latencies). @@ -83,15 +83,19 @@ def graph_iterative(data: pd.DataFrame, sel: list, layout: dict, :param layout: Layout of plot.ly graph. :param normalize: If True, the data is normalized to CPU frequency Constants.NORM_FREQUENCY. - :param data: pandas.DataFrame - :param sel: list - :param layout: dict - :param normalize: bool + :param remove_outliers: If True the outliers are removed before + generating the table. + :type data: pandas.DataFrame + :type sel: list + :type layout: dict + :type normalize: bool + :type remove_outliers: bool :returns: Tuple of graphs - throughput and latency. :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure) """ - def get_y_values(data, y_data_max, param, norm_factor, release=str()): + def get_y_values(data, y_data_max, param, norm_factor, release=str(), + remove_outliers=False): if param == "result_receive_rate_rate_values": if release == "rls2402": y_vals_raw = data["result_receive_rate_rate_avg"].to_list() @@ -100,6 +104,17 @@ def graph_iterative(data: pd.DataFrame, sel: list, layout: dict, else: y_vals_raw = data[param].to_list() y_data = [(y * norm_factor) for y in y_vals_raw] + + if remove_outliers: + try: + q1 = percentile(y_data, 25, method=C.COMP_PERCENTILE_METHOD) + q3 = percentile(y_data, 75, method=C.COMP_PERCENTILE_METHOD) + irq = q3 - q1 + lif = q1 - C.COMP_OUTLIER_TYPE * irq + uif = q3 + C.COMP_OUTLIER_TYPE * irq + y_data = [i for i in y_data if i >= lif and i <= uif] + except TypeError: + pass try: y_data_max = max(max(y_data), y_data_max) except TypeError: @@ -142,7 +157,12 @@ def graph_iterative(data: pd.DataFrame, sel: list, layout: dict, y_units.update(itm_data[C.UNIT[ttype]].unique().tolist()) y_data, y_tput_max = get_y_values( - itm_data, y_tput_max, C.VALUE_ITER[ttype], norm_factor, itm["rls"] + itm_data, + y_tput_max, + C.VALUE_ITER[ttype], + norm_factor, + itm["rls"], + remove_outliers ) nr_of_samples = len(y_data) @@ -192,7 +212,8 @@ def graph_iterative(data: pd.DataFrame, sel: list, layout: dict, itm_data, y_band_max, C.VALUE_ITER[f"{ttype}-bandwidth"], - norm_factor + norm_factor, + remove_outliers=remove_outliers ) if not all(pd.isna(y_band)): y_band_units.update( @@ -221,7 +242,8 @@ def graph_iterative(data: pd.DataFrame, sel: list, layout: dict, itm_data, y_lat_max, C.VALUE_ITER["latency"], - 1 / norm_factor + 1 / norm_factor, + remove_outliers=remove_outliers ) if not all(pd.isna(y_lat)): customdata = list() |